The growing use of Romanized Sinhala in digital communication platforms brings significant challenges to natural language processing tasks, particularly in backward transliterations which is the process of converting Romanized Sinhala text into the native Sinhala script. Since Romanized Sinhala is an informal way to represent native Sinhala, it lacks standardized spelling conventions leading to ad-hoc typing variations in which users frequently omit vowels, apply inconsistent phonetic spellings and use alternative consonants. This inconsistency exacerbates the problem of lexical ambiguity making it difficult to interpret meanings from Romanized Sinhala text. Existing back-transliteration systems struggle with these ad-hoc typing variations and word sense disambiguation, leading to significant accuracy loss. To address these challenges in existing back-transliteration systems, this research introduces a novel context-aware hybrid approach that combines an ad-hoc transliteration dictionary and rule-based approach with BERT-based language model trained on native Sinhala text. The proposed system was evaluated for backward transliteration using Sinhala BERT and its fine-tuned variant, achieving BLEU scores of around 0.91 with remarkably low Word Error Rate and Character Error Rate, approximately 0.09 and 0.02 respectively. Additionally, the first ever Word Sense Disambiguation (WSD) dataset for Romanized Sinhala is introduced as a part of this research. The proposed transliterator achieved an overall F1 score of approximately 0.94 highlighting the effectiveness of the proposed approach in handling ambiguous words in Romanized Sinhala.